{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "gross-apache",
   "metadata": {},
   "source": [
    "# 测试验证彩票预测的模型-1"
   ]
  },
  {
   "cell_type": "raw",
   "id": "classical-volleyball",
   "metadata": {},
   "source": [
    "第1种模型：\n",
    "当做回归问题预测\n",
    "7个号码对应7种模型,以第1个数为例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "medieval-chile",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 依赖导入\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "pacific-joshua",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Songti SC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "million-supplement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th>third</th>\n",
       "      <th>fourth</th>\n",
       "      <th>fifth</th>\n",
       "      <th>sixth</th>\n",
       "      <th>seventh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-05-30</th>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-02</th>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-04</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-06</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-06-09</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            first  second  third  fourth  fifth  sixth  seventh\n",
       "date                                                           \n",
       "2007-05-30     22      24     29      31     35      4       11\n",
       "2007-06-02     15      22     31      34     35      5       12\n",
       "2007-06-04      3       4     18      23     32      1        6\n",
       "2007-06-06      6      10     16      17     25      2        4\n",
       "2007-06-09      1       9     19      20     30      2       11"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../sorted_data.csv\", index_col=\"date\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "proved-appearance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取1号球\n",
    "first_ball_num = data.loc[:, \"first\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "criminal-berkeley",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据归一化\n",
    "first_ball_normal = (first_ball_num - 1)/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ordinary-mystery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1号球前50条数据展示\n",
    "fig1 = plt.figure()\n",
    "plt.plot([i for i in range(50)], first_ball_normal[:50])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "criminal-broadcast",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据抽取方法\n",
    "def data_extract(data, time_step=10):\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(len(data) - time_step):\n",
    "        x.append([a for a in data[i: i + time_step]])\n",
    "        y.append(data[i+time_step])\n",
    "    x = np.array(x).reshape(len(x), len(x[0]), features)\n",
    "    y = np.array(y).reshape(len(y), 1)\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "sized-security",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据拆分两部分（训练集和测试集）\n",
    "features = 1\n",
    "time_step = 5\n",
    "slide = int((len(data)*0.8))\n",
    "train_data = first_ball_normal.iloc[:slide]\n",
    "test_data = first_ball_normal.iloc[slide:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "demanding-pleasure",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽取数据\n",
    "x_train, y_train = data_extract(train_data, time_step)\n",
    "x_test, y_test = data_extract(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "congressional-supplier",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 50)                10400     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 10,451\n",
      "Trainable params: 10,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 建立LSTM模型\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=50, input_shape=(5, 1), activation=\"relu\"))\n",
    "model.add(Dense(units=1, activation=\"linear\"))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "comic-western",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\", metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "liberal-implementation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "27/27 [==============================] - 1s 3ms/step - loss: 0.0465 - mse: 0.0465\n",
      "Epoch 2/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0296 - mse: 0.0296\n",
      "Epoch 3/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0296 - mse: 0.0296\n",
      "Epoch 4/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0295 - mse: 0.0295\n",
      "Epoch 5/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0274 - mse: 0.0274\n",
      "Epoch 6/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0290 - mse: 0.0290\n",
      "Epoch 7/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0293 - mse: 0.0293\n",
      "Epoch 8/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0287 - mse: 0.0287\n",
      "Epoch 9/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0295 - mse: 0.0295\n",
      "Epoch 10/2000\n",
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     ]
    },
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     ]
    },
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     ]
    },
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
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     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0253 - mse: 0.0253\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0231 - mse: 0.0231\n",
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     ]
    },
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     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0226 - mse: 0.0226\n",
      "Epoch 1011/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0210 - mse: 0.0210\n",
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     ]
    },
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     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0196 - mse: 0.0196\n",
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      "Epoch 1259/2000\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0189 - mse: 0.0189\n",
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     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 1758/2000\n",
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     ]
    },
    {
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     "text": [
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      "Epoch 1923/2000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0137 - mse: 0.0137\n",
      "Epoch 1924/2000\n",
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      "Epoch 1994/2000\n",
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      "Epoch 1996/2000\n",
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      "Epoch 1997/2000\n",
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      "Epoch 1998/2000\n",
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      "Epoch 1999/2000\n",
      "27/27 [==============================] - 0s 2ms/step - loss: 0.0129 - mse: 0.0129\n",
      "Epoch 2000/2000\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcf8accb2d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, batch_size=64, epochs=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "loving-comparison",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict = model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "opened-slovakia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练数据效果展示\n",
    "fig2 = plt.figure()\n",
    "plt.plot(y_train[:50], label=\"训练数据\")\n",
    "plt.plot(y_train_predict[:50], label=\"预测的训练数据\")\n",
    "plt.title(\"训练数据效果展示\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "nuclear-spending",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "graduate-excuse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试数据效果展示\n",
    "fig3 = plt.figure()\n",
    "plt.plot(y_test[:50], label=\"测试数据\")\n",
    "plt.plot(y_test_predict[:50], label=\"预测的测试数据\")\n",
    "plt.title(\"测试数据效果展示\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "exotic-orlando",
   "metadata": {},
   "source": [
    "就结果来看，貌似回归问题不适合预测彩票，明日继续"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
